This is a small app that I built while learning Python and FastApi. It allows to create documentations (shared schemas + screenshots) from tracking events. It also serves as a backend for an interface that lets a user register, sign in, set some settings and view documentations.

While building this app, I spent the most time learning the best practices of accomplishing common tasks in the FastAPI ecosystem. I tried out various libraries and arrived at the following list that I think work well together:

• FastAPI + Asyncio
• SQLModel for data models and validation
• Celery for background tasks
• Alembic for migrations
• Supertokens for user authentication
• Mypy for type checking
• Make for running commands

Additionally, the codebase showcases the following things:

• Multiple FastAPI apps in one project
• Alembic auto-generating migrations from model files
• Celery tasks communicating through an SQS FIFO queue
• AWS S3 integration for storing images
• Loggers that print SQL queries and other useful information
• Error handler middlewares
• CORS middleware
• Two separate types of authentication (static auth token for an internal endpoint + Supertokens for user authentication)
• Project dependencies using requirements.txt
• And more.

I took a ton of inspiration from the following articles and projects:

Abstracting FastAPI Services

grillazz/fastapi-sqlalchemy-asyncpg

The ultimate async setup: FastAPI, SQLModel, Alembic, Pytest

The project is organized into the following directories and files:

• Python 3.10
• Supertokens account
• GitHub OAuth app (SuperTokens uses GitHub OAuth but that can easily be changed for another OAuth provider)
• AWS Account, an S3 bucket and an SQS queue
• PostgreSQL database

1. Clone the repo
2. Change sqlalchemy.url in alembic.ini to point to your database
3. Use commands in Makefile to install dependencies and run migrations
4. Rename .env.example to .env and fill in the values
5. Run make server and make worker to start the web server and the Celery worker

MIT License